{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8))\n",
    "x = [1, 2, 3, 4, 5, 6]\n",
    "y = [3, 6, 3, 5, 3, 10]\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8), dpi=100)\n",
    "x = [1, 2, 3, 4, 5, 6]\n",
    "y = [3, 6, 3, 5, 3, 10]\n",
    "plt.plot(x, y)\n",
    "plt.savefig('test.png')\n",
    "plt.show()  # 进行释放资源"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 60)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = range(60)\n",
    "y = [random.uniform(10, 15) for i in x]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[11.190749547358628,\n",
       " 13.446968652527664,\n",
       " 13.474581177970624,\n",
       " 14.012208773650338,\n",
       " 12.8725662832076,\n",
       " 14.407041447148059,\n",
       " 11.840235047029994,\n",
       " 12.857156331957166,\n",
       " 11.000173032015395,\n",
       " 10.396501293669859,\n",
       " 13.408760653445585,\n",
       " 14.881674108973709,\n",
       " 13.987606511530638,\n",
       " 11.665115466343511,\n",
       " 14.381595078846605,\n",
       " 10.605112334156209,\n",
       " 11.871472494517871,\n",
       " 12.161628078138637,\n",
       " 12.627936043322014,\n",
       " 11.503110710958287,\n",
       " 12.444573993388378,\n",
       " 12.152485441717362,\n",
       " 14.168155192764559,\n",
       " 12.194067249957524,\n",
       " 14.756460509335431,\n",
       " 14.415988430955148,\n",
       " 14.481305362218233,\n",
       " 14.240489012713377,\n",
       " 14.655466298784711,\n",
       " 12.546367520403333,\n",
       " 13.341473875013596,\n",
       " 12.198370334889294,\n",
       " 13.887636166122451,\n",
       " 12.145901769765313,\n",
       " 10.51278753953571,\n",
       " 13.539520730375479,\n",
       " 10.965390717624267,\n",
       " 14.14718470768278,\n",
       " 11.167192389421068,\n",
       " 14.156103632039574,\n",
       " 11.599881805106184,\n",
       " 13.531115247375368,\n",
       " 11.552777777240308,\n",
       " 11.050158979434288,\n",
       " 13.276564420491868,\n",
       " 11.026667764977429,\n",
       " 13.477038312284673,\n",
       " 12.065037542796482,\n",
       " 13.213246552011071,\n",
       " 10.842264009347709,\n",
       " 13.377910422462088,\n",
       " 11.50417998365542,\n",
       " 10.751117869552022,\n",
       " 10.763955214521927,\n",
       " 11.407192176166834,\n",
       " 14.42229429190064,\n",
       " 13.117775652209723,\n",
       " 10.749608406960185,\n",
       " 12.910769244952888,\n",
       " 14.160118766617867]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "ConversionError",
     "evalue": "Failed to convert value(s) to axis units: ['11点0分', '11点5分', '11点10分', '11点15分', '11点20分', '11点25分', '11点30分', '11点35分', '11点40分', '11点45分', '11点50分', '11点55分']",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mconvert_units\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m   1522\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1523\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconverter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1524\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\category.py\u001b[0m in \u001b[0;36mconvert\u001b[1;34m(value, unit, axis)\u001b[0m\n\u001b[0;32m     49\u001b[0m             raise ValueError(\n\u001b[1;32m---> 50\u001b[1;33m                 \u001b[1;34m'Missing category information for StrCategoryConverter; '\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     51\u001b[0m                 \u001b[1;34m'this might be caused by unintendedly mixing categorical and '\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Missing category information for StrCategoryConverter; this might be caused by unintendedly mixing categorical and numeric data",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mConversionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-38735aeb0855>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_ticks\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;31m# plt.xticks(x[::5], x_ticks[::5])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_ticks\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mxticks\u001b[1;34m(ticks, labels, **kwargs)\u001b[0m\n\u001b[0;32m   1652\u001b[0m                             \"without setting 'ticks'\")\n\u001b[0;32m   1653\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1654\u001b[1;33m         \u001b[0mlocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1655\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1656\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mget_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mowner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__module__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    449\u001b[0m                 \u001b[1;34m\"parameter will become keyword-only %(removal)s.\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m                 name=name, obj_type=f\"parameter of {func.__name__}()\")\n\u001b[1;32m--> 451\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    452\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    453\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mset_ticks\u001b[1;34m(self, ticks, minor)\u001b[0m\n\u001b[0;32m   1809\u001b[0m         \"\"\"\n\u001b[0;32m   1810\u001b[0m         \u001b[1;31m# XXX if the user changes units, the information will be lost here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1811\u001b[1;33m         \u001b[0mticks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_units\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1812\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1813\u001b[0m             \u001b[0mxleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxright\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mconvert_units\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m   1524\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1525\u001b[0m             raise munits.ConversionError('Failed to convert value(s) to axis '\n\u001b[1;32m-> 1526\u001b[1;33m                                          f'units: {x!r}') from e\n\u001b[0m\u001b[0;32m   1527\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1528\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: ['11点0分', '11点5分', '11点10分', '11点15分', '11点20分', '11点25分', '11点30分', '11点35分', '11点40分', '11点45分', '11点50分', '11点55分']"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8), dpi=100)\n",
    "\n",
    "plt.plot(x, y )\n",
    "\n",
    "# 添加自定义刻度\n",
    "x_ticks = [f'11点{i}分' for i in x]\n",
    "y_ticks = range(40)\n",
    "plt.yticks(y_ticks[::5])\n",
    "plt.xticks(x[::5], x_ticks[::5])\n",
    "# plt.xticks(x_ticks[::5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
